University of Cambridge >  Department of Engineering >  Information Engineering  >  Computational and Biological Learning Lab  > David Knowles

Matlab vs. R performance benchmarking

A somewhat outdated comparison of Matlab and R (and a variety of other packages) can be found here. Unfortunately the R code istoo outdated to run with the current (2.8.1) version but you can find a more up to date version here.

I've added five statistical functions tests to the benchmark. The Matlab code is here and the R code is here.

Set up

Each test was run twenty times on a Intel Core 2 1.86Ghz machine with4Gb RAM running Windows Vista Business. I increased the number ofrepeats from three to twenty since most of the calculationsrely on random numbers which will be different in each program. A muchbetter solution for the matrix calculations would be to use the samerandomly generated matrices ineach program, maybe I'll do that sometime!
I was using Matlab 2008b and R 2.8.0.

What conclusions can we draw?

Matlab is still on an order of twice as fast as R for Matrixoperations. Also, Matlab can sometimes workout how to vectorise loops -which is why the loop test here concludes that Matlab is 400 timesfaster than R! I should really write a new loop test that Matlab isn'table to vectorise! For statistical functions it is less clear cut.

Naturally, speed is not necessarily a key consideration. We'd allbe using C if it was after all. Personally, I prefer R's flexibleplotting, rich data structures, package management and free-ness. ButI'll admit it's far from perfect. But I'll admit it's far from perfect,and Matlab does have a lot of great features (like the profiler) andclean syntax.

I. Matrix calculation	R (sec)	Matlab (sec)	R/Matlab
Creation, transp., deformation of a 1500x1500 matrix	0.371	0.175	2.114
800x800 normal distributed random matrix 1000	0.179	0.135	1.323
Sorting of 2,000,000 random values	0.421	0.354	1.189
700x700 cross-product matrix	0.543	0.069	7.891
Linear regression over a 600x600 matrix	0.416	0.063	6.654
II. Matrix functions
FFT over 800,000 random values	0.443	0.129	3.441
Eigenvalues of a 320x320 random matrix	0.378	0.452	0.835
Determinant of a 650x650 random matrix	0.157	0.065	2.399
Cholesky decomposition of a 900x900 matrix	0.256	0.087	2.954
Inverse of a 400x400 random matrix	0.105	0.047	2.223
III. Programmation
750,000 Fibonacci numbers calculation (vector calc)	0.349	0.292	1.195
Creation of a 2250x2250 Hilbert matrix (matrix calc)	0.575	0.269	2.137
Grand common divisors of 70,000 pairs (recursion)	0.470	0.075	6.230
Creation of a 220x220 Toeplitz matrix (loops)	0.290	0.001	402.108
Escoufier's method on a 37x37 matrix (mixed)	0.550	0.636	0.865
IV. Statistics
Generating 107 uniform(0,1) random numbers	1.136	0.338	3.364
Create a permutation of [1..106 ]	0.078	0.257	0.304
Sample from binomal(100,.5) 105 times	0.061	0.480	0.126
Sample from N(0,1) 106 times	0.334	0.036	9.163
Evaluate N(0,1) at 106 locations	0.558	0.077	7.240